Data Science Approaches to Prevent Failure in Systems Engineering
Abstract
This technical report documents progress under SERC RT-206 between June 15th, 2018 (task order start date) and June 14th, 2019 (task order completion date). The primary motivation for this research effort is a pressing need to identify ways of tracking project risk to prevent future systems engineering failures, while advances in data science approaches and neural network applications are the enablers. Our work focuses on developing automated ways of tracking project risk based on two types of readily available information: enterprise software-derived data (Company inputs) and employee data collected via an app (Crowd inputs). The Company inputs carry risk information related to the daily operations of the organization (e.g., inventory data, number of failed parts, or financial data). We augment the database with Crowd inputs because we want to know what the people in the organization are doing to contribute to project risk. The underlying principle of our process is to collect these inputs continuously, frequently, and efficiently, and then process them using machine learning algorithms to predict failures. By predicting failures, we can make decision makers aware of the current risk of the projects in their organization, therefore giving them the opportunity to react before a failure occurs. In this effort, we focused on developing the main functions of the failure prediction prototype and evaluating whether our approach is a valid process to measure risk. We did so by testing our prototype and process in engineering student teams at Purdue University.
Document Details
- Document Type
- Technical Report
- Publication Date
- Jun 14, 2019
- Accession Number
- AD1075118
Entities
People
- Bruno Ribeiro
- Georgios Georgalis
- Karen Marais
- Leonardo De Abreu Cotta
Organizations
- Purdue University
- Systems Engineering Research Center